You’ve probably heard about the power of predictive analytics, and how it can practically save the world. Although there’s plenty of hyperbole to go around, there’s no denying the impact of this business intelligence tool. The right predictive analytics software can be one of the most important tools for a business.
Don’t yet believe in predictive analytics? Take a look at Amazon, says former Eloqua CMO Brian Kardon. In an interview with Heinz Marketing, Kardon explained the power of predictive analytics by pointing to Amazon’s success: “The best example is Amazon. Its recommendations are predictive analytics in action… More than 30% of its business comes through its recommendation engine. It is analyzing current and historical data to make predictions about the future … with stunning results.”
So, yes, predictive analytics can make a huge difference in a business. But this difference only happens with proper implementation, which isn’t always the case. There are a lot of analytic solutions in the predictive analytics software market, so it can be tough to find the right one. If you’re going to be implementing predictive analytics software, make sure you know how to do it the right way. Conveniently, we have a list of five ways to do just that:
Separate Your Needs and Your Wants
Before you even think about looking at vendors, you need to figure out which features you need, and which are nice-to-haves. This helps start your search off on the right foot in two very important ways. First, you can eliminate any vendors right off the bat that don’t have one of your needs. Second, you’re less likely to get hooked by some fancy bells and whistles that look nice, but don’t provide real value. For example, your business probably needs statistical analysis capabilities, but does it need artificial intelligence? Although there are a lot of great things AI can do, it may not be worth an investment right away. Having a list made in advance helps make decisions like these easier.
Assess Your Data
Even the best analytic solutions can’t make up for bad data. Kissmetrics offers up a couple revealing stats about the benefits of clean data. Businesses save about 5% of their revenue annually, and can generate up to 70% more revenue based solely on clean data. So before you start getting price quotes from vendors, make sure you have the right data for predictive analytics software. Some questions to ask yourself (or your IT department) include: do you have enough data? Is this quality data? Is this the right kind of data we need? And if you don’t have enough high quality data, can you use external data to supplement what you have?
Create a Success Timeline
Ali Rahim at Information Builders suggests identifying the criteria you’ll use to assess your predictive analytics software. Use that criteria to make a timeline of small, incremental goals to aim for. Setting incremental goals rather than one large goal helps avoid the frustration of not seeing a massive change overnight. By doing so, your employees know what the goals are and can make decisions with them in mind. Louis Columbus says “The best selection processes are anchored in specific business goals, defining exactly what the expected contribution from the application is.” This also simplifies the evaluation process following implementation. All you have to do is compare your current ROI to what your goal was.
Get Everyone on Board
One of the biggest reasons that software implementations fail is because they aren’t fully embraced by employees. To make sure this doesn’t happen, get everyone who needs to be on board with using predictive analytics. Even if they’re hesitant at first, show them the data and what it can do to help your growth. This involves making sure every user actually uses the software and its insights for decision-making. By doing so, your business as a whole works cohesively, as everyone uses the same methods to achieve the same goal.
Make a Change Management Plan
As we detailed in 52 Software Selection Tips, change management is one of the most important steps in implementation. After you’ve selected a vendor and nailed down a contract, it’s time for the transition. Changing your processes to accommodate big data and predictive models doesn’t have to be mind numbing. Executing a transition like this is never easy, but it can be pretty seamless with proper planning. A large part of change management involves the training of all of your users. Starting training before the transition begins helps iron out any wrinkles and makes sure that all your users are ready to go from day one.
With the importance of predictive analytics today, no business should risk an unsuccessful implementation. The available insights are too valuable to lose out on. But, if you follow these five steps you’ll have nothing to worry about.